where \(Sigmoid(\mathchoice{\mathbin {\hbox{\scalebox {.5}{$\m@th \displaystyle \bullet $}}}}{}{}{}\) )\( is the sigmoid function, which ensures the attention values are in range \) [0,1]\(. After that, the value results are multiplied with the attention map to capture the correlation with each pixel. By finally being added a skip connection to avoid the problem of vanishing gradient, the output \) Y RN Cf\( can be defined as\begin{equation}\mathbf {Y}=\tilde{\mathbf {A}}\mathbf {V}+\mathbf {X}\end{equation}\) To capture the context prior of pixels, we adopt the method of [1]} to generate the ideal attention map. As it can be seen in REF , given the ground truth , we can know the label of each pixel. To transform it into the relation between different pixels, the ground truth is first down-sampled into the size of \(H\times W\)  and then flattened into a vector \(\mathbf {m}\)  of size \(1\times N\) . After the one-hot encoding implementation, new binary columns which indicate the presence of value from the ground truth are created, leading to a \(H\times W\times C\)  matrix \(\mathbf {M}\) , where \(C\)  represents the number of total categories. The matrix \(\mathbf {M}\)  is then reshaped into size of \(N\times C\)  and finally the ideal attention map \(\mathbf {A}\)  is constructed with
\(\mathbf {A}=\mathbf {M}\mathbf {M}^{T}.\) 
